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What is video annotation?
Video annotation is the process of adding descriptive metadata, or labels, to a video. It is similar to image annotation; adding metadata descriptions to a video is the only difference. Such an approach can provide a contextual understanding of the video’s content and greatly benefits video processing. Video annotation can involve identifying and labeling objects, people, actions, or events within the video.
Being used in the branch of computer vision, video annotation can also leverage machine learning algorithms to enhance the annotation task. Since ML algorithms are good at extracting visual information, leveraging ML in video annotation can offer a wide range of applications in real-life scenarios. Essentially, helping analysts to interpret visual information pertaining to a particular task quickly and efficiently.
For example, video annotation can be used to train a computer vision algorithm to recognize different types of objects or to track the movements of people within a video.
Different approaches for video annotation
Generally, video annotation can be done manually, which involves watching videos and adding metadata manually on a frame-by-frame basis. Another approach involves using automated or AI-enhanced software to do the same task. But there are approaches that are derivative of these two major approaches. Let’s discuss some of these approaches briefly.
- Manual annotation: This approach involves manually watching the video and annotating the relevant information, such as objects, events, and actions, using a labeling tool or software. This is a tedious and time-consuming process. A manual annotation approach is important when the objects have to be precisely annotated or when there is a need for human expertise.
- Semi-automatic annotation: It involves a combination of both manual and automated annotation. In this process, the automated software annotates the relative video, which is then reviewed by a human annotator. This ensures the quality of the entire process.
- Crowdsourced annotation: This approach involves outsourcing the annotation task to a large group of individuals, such as online workers or volunteers, who annotate the video based on a set of guidelines. This approach is useful when large amounts of data need to be annotated quickly and cost-effectively.
- Active learning: This approach leverages machine learning algorithms to select the most informative data samples for annotation, reducing the amount of manual annotation required. The algorithm selects the samples that are most difficult for the algorithm to classify and presents them to a human annotator for labeling.
- Transfer learning: This approach involves using pre-existing annotated data to train an algorithm on a similar but different dataset, reducing the amount of manual annotation required. The pre-existing data can be used to train a model on certain features, which can then be transferred to the new dataset for further training.
Types of video annotation
Video annotation can be of several types. Some are similar to what we find in image annotation; these include:
- Object annotation involves identifying and labeling objects in the video, such as cars, people, animals, buildings, et cetera.
- Event annotation identifies and labels specific events or actions that occur in the video, such as someone walking, a car driving, or a person entering a building. This is especially useful in surveillance and security. It also involves time labels.
- Semantic segmentation like image segmentation, involves applying a pixel-level color mask with a semantic category. This is useful in identifying different parts of a scene or object. This is similar to what we find in image segmentation.
- Pose estimation uses algorithms to track an object, especially a person, in the real world. This involves labeling the position and orientation of human joints, such as the position of the head, arms, and legs.
- Emotion recognition identifies and labels emotions expressed by people in the video, such as happiness, sadness, anger, or fear.
- Activity recognition identifies and labels different activities or actions performed by people in the video, such as walking, running, dancing, or cooking.
- Audio annotation detects and labels sound or speech in the video, such as music, dialogue, or background noise.
Application of Video annotation
Now let’s briefly explore the application where video annotation is useful.
Autonomous vehicles
One of the most useful applications of video annotation is in autonomous vehicles. It can be used to train computer vision models to identify, classify and respond to different objects and situations on the road, such as traffic signs, pedestrians, and other vehicles. These vehicles need a continuous feed of information to make informed decisions.
Surveillance
Video annotation can be used to detect and track individuals, objects, and activities in security camera footage, helping to prevent crime and improve public safety. Some corporations also use face detection to amplify their security. Surveillance cameras are heavily used in airports to monitor traffic and all high-security places.
Sports analysis
Sports analysis is another area where video annotation is used. It is used to analyze and understand the performance of athletes, such as tracking their movements and identifying areas for improvement.
Medical diagnosis
Video annotation can be used to analyze medical imaging data, such as identifying and tracking cancer cells in medical images or analyzing the movement of muscles and joints for physical therapy.
Face Recognition
Face recognition is widely used to unlock phones and iPads. In addition, new security standards have been developed that can recognize a face even when wearing a mask. This was seen when Apple introduced face id to unlock iPhones with masks on.
Robotics
One industrial robot with built-in computer vision is equivalent to several blind robots in terms of productivity. A clever robot can easily move objects from one location to another while avoiding humans and other robots. Video annotation can be used to train such robots to recognize and interact with their environment, such as identifying objects to pick up or avoiding obstacles.
Pros and Cons of Video Annotation Outsourcing Services
Video annotation outsourcing is the process of hiring a third-party service provider to annotate videos, i.e., add metadata or descriptive labels to the video data; it is similar to image annotation services. Machine learning models must be trained using this approach in order to be used in a variety of applications, including object identification, facial recognition, speech recognition, and others. These are some benefits and drawbacks of outsourcing services for video annotation:
Pros:
- Cost-effective: Outsourcing video annotation services can be cost-effective for businesses as it saves the cost of setting up an in-house team and the infrastructure required for video annotation.
- Access to Skilled Workforce: A competent staff with experience in annotating videos can be accessed through outsourcing video annotation services, resulting in higher-quality output.
- High-Quality Dataset: Outsourcing video annotation to a group of expert annotators can prevent errors like misaligned bounding boxes or segmentation masks, which need time and expertise to identify, let alone repair.
As videos are more complicated than photos, annotating them is more difficult. In particular, businesses frequently seek to outsource video annotation to have access to the necessary tools and methods. - Flexibility: With regard to the amount of work, outsourcing video annotation services offers freedom. Organizations can adjust the number of videos to be annotated according to their needs.
- Faster Turnaround Time: Third-party service providers offer specialized teams to work on the annotations, and outsourcing video annotation services can lead to quicker response times. It can also lead to scalability.
Cons:
- Data Security Concerns: Outsourcing video annotation services could compromise data security because the third-party service provider might get access to sensitive information like video footage.
- Lack of Control: The decision to outsource video annotation services can lead to a situation where the business has little authority over the quality of the annotations since the service provider may not follow the same quality benchmarks as the business.
- Communication Challenges: Outsourcing video annotation services to service providers in different time zones may result in communication challenges, leading to delays in the delivery of annotated videos.
- Dependency on Service Provider: Outsourcing video annotation services may result in dependency on the service provider for future annotations, leading to challenges in case of service provider unavailability or business requirement changes.
Best Practices for Video Annotation Outsourcing Services
Outsourcing video annotation is the standard norm in the modern tech world. In fact, it has become so commonplace that it may be necessary to argue against outsourcing rather than in favor of it.
This is a list of procedures that, if followed exactly, should allow you to fully utilize video annotation and achieve the goals for which it was intended. Keep in mind that giving your vendor the tools they need to execute a successful job will ultimately serve your interests.
Understand Your Requirements
Before considering whether to outsource video annotation, it is crucial to determine if it is needed in your business. In other words, what is the purpose of annotating video data, and what are the desired outcomes? Once this need has been established, deciding whether to perform the annotation in-house or outsource it becomes a matter of detail.
Keep in mind that the final outcome lies with the business, and it should be driven by their needs. It is important to consider if video annotation will solve a problem, add value, and what criteria will be used to determine its success or failure.
Reach out to Vendors
The next step is to evaluate potential vendors, but this can be challenging due to information asymmetry, where not everyone has access to all relevant information.
To address this, it’s recommended to follow a Request for Proposal (RFP) process or a similarly disciplined approach. This involves putting the word out in the community in a way that provides relevant information about the work to be outsourced without revealing confidential details, such as a brief description, expected volume of work, timelines, technology/tools to be used, and qualifications of required manpower.
Doing so limits responses to genuine parties with a real interest in doing the work rather than receiving irrelevant applications based on incorrect assumptions. This approach can save time and effort in reviewing and eventually eliminating unsuitable responses, ensuring that only genuine and interested parties are considered for the work.
Vendor Selection, Pilot Run, and Contract
This step involves selecting the right vendor for your task. In hopes that you have given a proper description of your task, choose the vendor that appeals to you the most. Evaluate the vendors by asking them for samples of their recent works and reasonable quotations as well. Once satisfied, discuss your project in greater detail and schedule a pilot project.
Evaluate the pilot project and find the pros and cons of the vendor. Discuss the areas where the vendor didn’t perform well. Once satisfied, you can then finalize the project and sign a contract.
Developing and Implementing the Project Plan
Once the organization is ready, a detailed Project Plan can be created for both parties. The Project Plan will establish various tasks’ timelines, responsibilities, dependencies, checkpoints, control mechanisms, and other variables. In other words, it will mainstream the work envisaged in the project.
Scale Up
When you outsource video annotation, it’s common for there to be a gradual progression towards managing higher volumes of data with each day and week. This eventually leads to additional people becoming trained and accessible, and the vendor gains confidence in the new project. Over time, the work will gradually scale up to handle the specified volumes. In most instances, this is also covered by the contract and project plan.
Conclusion
In conclusion, video annotation is a crucial process in computer vision that involves adding descriptive metadata or labels to a video. It provides a contextual understanding of the video’s content and greatly benefits video processing.
While outsourcing video annotation has advantages, such as reducing the costs and time required, there are also some drawbacks, including potential privacy and security concerns. Therefore it is always wise to understand your requirements and find vendors that adhere to your norms, privacy, and specificity.
